基于Logistic回归模型的砂土液化概率评价

以国内外23次地震中200组场地液化实测数据为基础,通过Logistic回归分析,建立关联修正标准贯入击数N160cs与循环应力比CSR的液化概率模型。以50 %液化概率水平为液化与非液化的临界点,建立了指数形式的抗液化应力比CRR计算式,新建概率模型预测饱和砂土液化与非液化的成功率分别为85.71 %和76.14 %,具有较高的可靠性。与已有模型比较,使用了新的数据和修正系数,消除了一些不合理的偏差,总体判别结果偏于安全。为了将确定性分析方法与概率分析方法联系起来,建立了抗液化安全系数FS与液化概率PL的关系式。算例结果表明,新建概率模型简单、实用、可靠。     

地震液化条件下地面的大变形三维数值分析

地基液化条件下地面大变形是造成工程结构破坏的主要原因之一。考虑地形、地震、土层、地下水等影响因素,针对典型的岸坡场地3层土地基模型,利用有限差分法FLAC3D,对可液化场地在地震作用下发生地面大变形的过程进行了数值模拟。结果表明,临空面坡比愈大、地表坡度越陡,地基液化地表侧向位移值愈大;变坡度的场地在地震作用下发生的侧移要比单一倾斜率的场地大;地震最大加速度越大、地震持续时间越长,地基液化侧向位移、地表沉陷和隆起现象越严重;液化层的埋深、厚度以及地下水位都对地面大变形的产生有着不同程度的影响,应选择合理的地基处理方案进行处理。     

基于应变空间多机构CG模型的地震液化大变形数值分析

砂土液化导致的地基侧向大变形是地震中许多重要的工程设施和建筑物破坏的主要原因之一。简要介绍了可进行液化大变形分析的散粒体材料本构模型--应变空间多机构CG模型,基于FLIP ROSE程序平台,建立了预测和研究倾斜地基砂土液化导致侧向大变形的二维有限元数值分析方法。采用该模型对相同工况的土工动态离心模型试验进行了模拟,通过对比超孔隙水压力、剪切波水平加速度以及地基侧向位移发现,数值预测与试验结果吻合良好,从而验证了该有限元数值分析模型的可靠性。最后利用该数值分析模型预测了倾斜率不同的地基受到相同剪切波作用时,倾斜地基不同深度产生的侧向位移。预测结果显示,随着地基深度的减小,倾斜率对于地震液化导致倾斜地基侧向大变形的影响越来越显著。     

渗透系数对砂土液化震陷影响的数值研究

饱和砂土在地震荷载的作用下往往会产生液化变形,包括竖向震陷和侧向扩展。砂土由于液化的作用,其渗透系数会发生改变,而目前描述砂土液化变形的本构模型均采用常量渗透系数,这是造成自由场地的震陷数值模拟结果低于试验观测值的重要原因之一。利用开源有限元平台Open Sees对饱和砂土的自由场地震陷进行模拟分析,对比离心机模型试验,分析了渗透系数对饱和砂土液化震陷的影响。为进一步提高数值模拟的准确性,采用了适合于动力分析及液化模拟的边界面模型。与固定渗透系数模型相比,最终提出的变渗透系数模型允许液化状态的渗透系数升高为初始值的数倍,该模型模拟结果较好,可以作为从渗透模型角度提高数值模拟精度的近似考虑。通过一系列的模拟和分析发现,采用合理的变渗透系数模型,可在一定程度上提高砂土自由场地地震液化震陷的数值模拟精度。     

基于地面运动强度及标准贯入试验的上海地区砂土地震液化评价

上海市地处长江三角洲前缘,黄浦江和苏州河交汇区域,特殊的地理环境与沉积环境导致浅部砂层广泛发育。随着城市建设的不断推进,上海城市区域范围的砂土地震液化风险评价成为亟待研究的课题。文章基于上海市工程钻孔数据,结合地震地面运动加速度分布与标准贯入试验,建立区域性地震液化危险性评价模型,对上海市进行了地震液化危险性评价。研究认为当发生50年超越概率10%的地震条件下,上海市陆域面积的66.0%将不会产生地震砂土液化灾害,21.8%的陆域面积仅发生轻微液化,只有崇明、横沙、长兴三岛,黄浦江及苏州河两岸地震液化等级达到中等甚至严重,占全市陆域面积12.3%;50年超越概率2%的地震条件下,随着峰值地面运动加速度整体升高,全市范围内轻微—严重液化区域明显增多,可能发生地震液化的总面积达到全市陆域面积46.25%。上海市存在砂土地震液化的危险性,但是发生概率较低。研究认为,目前的抗震设计规范中上海市的设防烈度偏高,可能导致不必要的建设成本。同时研究中的不同超越概率下的地震液化危险性评价结果为上海市工程建设相关标准的合理化改进的提供了建议和参考。      

砂性地基中防波堤地震残余变形机制分析与液化度预测法

海港工程中防波堤地震残余变形的预测以及震损机制的分析是较复杂的问题。采用定义在应变空间中考虑土体动主应力轴方向偏转影响的多重剪切机构塑性模型,分别从砂性土的黏粒含量和标贯击数2个主要影响因素对防波堤地震变形的变化机制进行了有效应力分析,得出防波堤较大的残余变形是由于地震作用下土体中孔隙水压力的升高致使土体软化而产生的。然后,采用液化度单一指标从物理本质上来间接表征防波堤的残余变形,得到防波堤残余变形与液化度之间的函数预测关系,预测值与震害调查值以及数值分析值基本相符,表明所得液化度预测公式具有一定的可靠性。残余变形的液化度预测法可为类似防波堤地震灾害设计与评价提供参考依据。     

地震荷载下辽西饱和粉土液化特性分析

地震荷载作用引起的粉土液化是路基抗震设计的重要问题。通过动三轴试验研究了不同围压、动剪应力条件下辽西地区饱和粉土的液化特性,并利用应变孔压模型对辽西饱和粉土路基进行了液化特性的数值模拟分析。结果表明:围压条件一定时,动剪应力与液化振次呈双曲线关系;抗震设防烈度为7度时,超越概率63.5%和10%的地震荷载不能使辽西饱和粉土发生液化,而超越概率2%时液化深度约为5 m左右,这为粉土路基抗震设计提供了一定的参考依据。     

考虑时变影响的拱坝坝肩空间变形场预测

坝肩变形是拱坝坝肩稳定状态的综合反映,在运行期呈小变形变化,监测点测值在空间上具有连续性,同时影响拱坝坝肩变形的各种因素具有时变性。通过融合统计回归和多层递阶方法的特点,建立了反映变形场空间和时变特性的空间时变分析模型。实例分析表明,该模型实用有效,预测精度高。考虑时变影响可以提高拱坝坝肩空间变形场的预测能力,对监控拱坝坝肩的实际工作状况有重要意义。     

砂土液化内部应力变化规律与工程液化判别

以往对砂土液化的研究主要侧重于水平场地、自由应力场条件下有关地基液化机理与判别等问题的研究。通常将Δu=σz=ΔUmax作为地基土液化的判据，而对工程结构物和场地条件的影响考虑不足。基于当前砂土液化问题的研究现状及工程特性，提出了将液化分为理论液化和工程液化。前者主要研究地基土液化的一般规律性问题；后者则针对具体工程结构物而言。其液化标准是以地基土在遭受地震液化时是否会导致工程结构物的破坏为依据。通过对砂土在震动液化过程中内部应力变化规律的理论分析，阐明了水平应力σx或σy对斜坡场地地基土发生侧向液化的作用机理，不能将斜坡场地的地基看作半无限空间体处理，提出了液化膨胀侧扩势Ψ的概念与计算式。指出：对斜坡场地，为避免这种侧向液化流动变形破坏，采取加强可液化土体的侧向约束、缩小偏应力差是必要的。根据工程结构物的承载力极限状态和正常使用极限状态，提出工程液化的判别准则：(1)可液化土体的地基强度τ降低到工程结构物所允许的强度值[τ，]；(2)可液化土体的膨胀侧扩势Ψ增加到其侧向约束强度[τh]；(3)可液化土体地基的变形s增大到工程结构物所允许的变形值[s]。     

基于MPL神经网络的地震作用下砂土液化评估及预测

砂土在地震的作用下会产生剧烈的液化现象,液化引发的地基失稳会对道路、建筑物、堤坝等各类设施造成严重危害。因此,地震作用下的砂土液化判别预测一直是地质灾害领域研究的热点问题。本文使用过去几十年发生在世界各地的166组地震作用下砂土液化实例数据,通过大量数据训练和参数分析建立了基于机器学习的地震作用下砂土液化判别模型。结果表明,当网络结构为6(输入层)-15(隐藏层)-1(输出层)、训练函数为Levenberg-Marquardt时,对地震液化预测效果较好,最大准确率可达96%。参数分析结果表明不同参数对网络预测准确率影响程度不一:锥端阻力、地表归一化峰值水平加速度影响相对较大;地震震级、总垂向应力、有效垂向应力影响中等;贯入深度对其影响较小。因此在不同网络预测精度要求的条件下,可考虑适当简化输入参数。     

Undrained lateral load capacity of piles in clay using artificial neural network

This paper describes the application of the artificial neural network model to predict the lateral load capacity of piles in clay. Three criteria were selected to compare the ANN model with the available empirical models: the best fit line for predicted lateral load capacity (Q_p) and measured lateral load capacity (Q_m), the mean and standard deviation of the ratio Q_p/Q_m and the cumulative probability for Q_p/Q_m. Different sensitivity analysis to identify the most important input parameters is discussed. A neural interpretation diagram is presented showing the effects of input parameters. A model equation is presented based on neural network parameters.     

On the modeling of ground-motion field for assessment of multiple-location hazard,damage, and loss: example of estimation of electric network performance during scenario earthquake

Consideration of within-earthquake ground-motion correlation is essential for the estimation of seismic hazards, damage, and loss for spatially distributed systems. In many seismically active regions, the strong motion data of real engineering significance are completely unavailable or very scarce. The absence of necessary data does not allow developing regional empirical correlation models, and the models obtained for other regions should be used with correspondent sensitivity analysis. The level of within-earthquake correlation may vary in broad range; therefore, development of correspondent criteria for selection from available models is important. In this paper, we analyzed the performance of a system of critical elements of electric power network (substations) depending on variations in within-earthquake correlation. The performance is described as probability of different levels of non-functionality, i.e., percentage of area suffering power outage, and probability of expected number of customers without power. We have shown that the proper choice of the within-earthquake correlation model is critical in comprehensive estimations of functionality of substations in electrical power system. Skipping the ground-motion variability and within-earthquake correlation may lead to unreliable results. Relevance of geology-based within-earthquake correlation models has been demonstrated, and a scheme, which allows reducing uncertainty in the choice of realistic correlation, has been proposed.     

DEM-based deterministic landslide hazard analysis in the Lesser Himalaya of Nepal

In Nepal, people live in widely spread settlements in the fragile Himalayan terrains, and suffer more from landslides than from any other type of natural disaster. The small-scale rainfall-triggered landslides in the Lesser Himalaya of Nepal are generally shallow (about 0.5 to 2.5 m) and are triggered by changes in the physical property of soil layers during rainfall. The relation between landslides and slope hydrology has received little attention in Himalayan landslide research. Thus, this paper deals with the probability of slope failure during extreme rainfall events by considering a digital elevation model (DEM)-based hydrological model for soil saturation depth and an infinite slope stability model. Deterministic distributed analysis in a geographic information system (GIS) was carried out to calculate the probability of slope failure. A simple method of error propagation was used to calculate the variance of the safety factors and the probability of failure. When normally distributed failure probability values were checked against existing landslides, it was found that more than 50% of the pixels of existing landslides coincided with a high calculated probability of failure. Although the deterministic distributed analysis has certain drawbacks, as described by previous researchers, this study concluded that the calculated failure probability can be utilised to predict the probability of slope failure in Himalayan terrain during extreme rainfall events.     

有限数据条件下边坡可靠度分析的Bootstrap方法

基于Bootstrap抽样技术提出了有限数据条件下边坡可靠度分析方法。简要介绍了传统的边坡可靠度分析方法。采用Bootstrap方法模拟了抗剪强度参数概率分布函数的统计不确定性。以无限边坡为例研究了抗剪强度分布参数和分布类型不确定性对边坡可靠度的影响规律。结果表明：基于有限数据估计的样本均值、样本标准差和AIC值具有较大的变异性,这种变异性进一步导致了抗剪强度参数概率分布函数存在明显的统计不确定性。在考虑抗剪强度参数概率分布函数的统计不确定性时,边坡可靠度指标应为具有一定置信度水平的置信区间,而不是传统可靠度分析中的固定值。边坡可靠度指标的置信区间变化范围随安全系数的增加而增大,同时考虑分布参数和分布类型不确定性计算的可靠度指标具有更大的变异性和更宽的置信区间变化范围。Bootstrap方法为有限数据条件下抗剪强度参数概率分布函数统计不确定性的模拟以及边坡可靠度的评估提供了一条有效的途径。     

Stochastic prediction in geomorphology using Bayesian inference models

Bayesian inference modeling may be applied to empirical stochastic prediction in geomorphology where outcomes of geomorphic processes can be expressed by probability density functions. Natural variations in process outputs are accommodated by the probability model. Uncertainty in the values of model parameters is reduced by considering statistically independent prior information on long-term, parameter behavior. Formal combination of model and parameter information yields a Bayesian probability distribution that accounts for parameter uncertainty, but not for model uncertainty or systematic error which is ignored herein. Prior information is determined by ordinary objective or subjective methods of geomorphic investigation. Examples involving simple stochastic models are given, as applied to the prediction of shifts in river courses, alpine rock avalanches, and fluctuating river bed levels. Bayesian inference models may be applied spatially and temporally as well as to functions of a random variable. They provide technically superior forecasts, for a given shortterm data set, to those of extrapolation or stochastic simulation models. In applications the contribution of the field geomorphologist is of fundamental quantitative importance.     

Piles in fully liquefied soils with lateral spread

The paper provides a new analysis procedure for the assessment of the lateral response of isolated piles/drilled shafts in saturated sands as liquefaction and lateral soil spread develop in response to dynamic loading such as that generated by the earthquake shaking. The presented method accounts for: (1) the development of full liquefaction in the free-field soil that could trigger the lateral spread of the overlying crust layer; (2) the driving force exerted by the crust layer based on the interaction between the pile and the upper non-liquefied soil (crust) layer; and (3) the variation of the excess pore water pressure (i.e. post-liquefaction soil strength) in the near-field soil with the progressive pile deflection under lateral soil spread driving force. A constitutive model for fully liquefied sands under monotonic loading and undrained conditions is developed in order to predict the zone of post-liquefaction zero-strength of liquefied sand before it rebounds with the increasing soil strain in the near-field. The analytical and empirical concepts employed in the Strain Wedge (SW) model allow the modeling of such a sophisticated phenomenon of lateral soil spread that could accompany or follow the occurrence of seismic events without using modifying parameters or shape corrections to account for soil liquefaction.     

Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields

In earth and environmental sciences applications, uncertainty analysis regarding the outputs of models whose parameters are spatially varying (or spatially distributed) is often performed in a Monte Carlo framework. In this context, alternative realizations of the spatial distribution of model inputs, typically conditioned to reproduce attribute values at locations where measurements are obtained, are generated via geostatistical simulation using simple random (SR) sampling. The environmental model under consideration is then evaluated using each of these realizations as a plausible input, in order to construct a distribution of plausible model outputs for uncertainty analysis purposes. In hydrogeological investigations, for example, conditional simulations of saturated hydraulic conductivity are used as input to physically-based simulators of flow and transport to evaluate the associated uncertainty in the spatial distribution of solute concentration. Realistic uncertainty analysis via SR sampling, however, requires a large number of simulated attribute realizations for the model inputs in order to yield a representative distribution of model outputs; this often hinders the application of uncertainty analysis due to the computational expense of evaluating complex environmental models. Stratified sampling methods, including variants of Latin hypercube sampling, constitute more efficient sampling aternatives, often resulting in a more representative distribution of model outputs (e.g., solute concentration) with fewer model input realizations (e.g., hydraulic conductivity), thus reducing the computational cost of uncertainty analysis. The application of stratified and Latin hypercube sampling in a geostatistical simulation context, however, is not widespread, and, apart from a few exceptions, has been limited to the unconditional simulation case. This paper proposes methodological modifications for adopting existing methods for stratified sampling (including Latin hypercube sampling), employed to date in an unconditional geostatistical simulation context, for the purpose of efficient conditional simulation of Gaussian random fields. The proposed conditional simulation methods are compared to traditional geostatistical simulation, based on SR sampling, in the context of a hydrogeological flow and transport model via a synthetic case study. The results indicate that stratified sampling methods (including Latin hypercube sampling) are more efficient than SR, overall reproducing to a similar extent statistics of the conductivity (and subsequently concentration) fields, yet with smaller sampling variability. These findings suggest that the proposed efficient conditional sampling methods could contribute to the wider application of uncertainty analysis in spatially distributed environmental models using geostatistical simulation.     

节理岩体关键块体稳定的概率分析

基于地下岩体受节理面的控制,节理面的几何和力学参数随机分布,从而导致岩体系统具有高度不确定性,提出以关键块体理论为基础,考虑节理几何和力学参数随机性的岩体开挖可靠度分析方法,并给出了块体稳定的总失效概率评价模型。以澳大利亚阿德莱德地区一铜矿地质条件为例,以节理面倾角、倾向、摩擦系数和黏聚力为随机变量,通过Monte Carlo模拟和概率图方法,进行了岩体可靠度和失效概率的计算。最后,采用条件概率的分析方法,计算了单面滑动块体的总失效概率。计算结果表明,块体沿单面滑动并且出现的概率为11.0%,总的失效概率为3.85%,超过一般岩体工程可允许的风险水平,认为该方法可以作为评价块体可靠性的依据。     

Fuzzy probabilistic seismic hazard analysis with applications to Kunming city,China

China is prone to highly frequent earthquakes due to specific geographical location, which could cause significant losses to society and economy. The task of seismic hazard analysis is to estimate the potential level of ground motion parameters that would be produced by future earthquakes. In this paper, a novel method based on fuzzy logic techniques and probabilistic approach is proposed for seismic hazard analysis (FPSHA). In FPSHA, we employ fuzzy sets for quantification of earthquake magnitude and source-to-site distance, and fuzzy inference rules for ground motion attenuation relationships. The membership functions for earthquake magnitude and source-to-site distance are provided based on expert judgments, and the construction of fuzzy rules for peak ground acceleration relationships is also based on expert judgment. This methodology enables to include aleatory and epistemic uncertainty in the process of seismic hazard analysis. The advantage of the proposed method is in its efficiency, reliability, practicability, and precision. A case study is investigated for seismic hazard analysis of Kunming city in Yunnan Province, People’s Republic of China. The results of the proposed fuzzy logic-based model are compared to other models, which confirms the accuracy in predicting the probability of exceeding a certain level of the peak ground acceleration. Further, the results can provide a sound basis for decision making of disaster reduction and prevention in Yunnan province.     

Method of Data Reduction and Uncertainty Estimation for Platinum-Group Element Data Using Inductively Coupled Plasma-Mass Spectrometry

A data reduction method is described for determining platinum-group element (PGE) abundances by inductively coupled plasma-mass spectrometry (ICP-MS) using external calibration or the method of standard addition. Gravimetric measurement of volumes, the analysis of reference materials and the use of procedural blanks were all used to minimise systematic errors. Internal standards were used to correct for instrument drift. A linear least squares regression model was used to calculate concentrations from drift-corrected counts per second (cps). Furthermore, mathematical manipulations also contribute to the uncertainty estimates of a procedure. Typical uncertainty estimate calculations for ICP-MS data manipulations involve: (1) Carrying standard deviations from the raw cps through the data reduction or (2) calculating a standard deviation from multiple final concentration calculations. It is demonstrated that method 2 may underestimate the uncertainty estimate of the calculated data. Methods 1 and 2 do not typically include an uncertainty estimate component from a regression model. As such models contribute to the uncertainty estimates affecting the calculated data, an uncertainty estimate component from the regression must be included in any final error calculations. Confidence intervals are used to account for uncertainty estimates from the regression model. These confidence intervals are simpler to calculate than uncertainty estimates from method 1, for example. The data reduction and uncertainty estimation method described here addresses problems of reporting PGE data from an article in the literature and addresses both precision and accuracy. The method can be applied to any analytical technique where drift corrections or regression models are used.